Java遺傳演算法之實現衝出迷宮的實例分析

這篇文章首先詳細介紹了什麼是遺傳演算法，然後透過遺傳演算法的思想用實例解析使用遺傳演算法解決迷宮問題,需要的朋友可以參考下

遺傳演算法是模擬達爾文生物進化論的自然選擇和遺傳學機制的生物演化過程的計算模型，是一種透過模擬自然演化過程來搜尋最優解的方法。它能解決很多問題，例如數學方程式的最大最小值，背包問題，裝箱問題等。在遊戲開發中遺傳演算法的應用也十分頻繁，不少的遊戲 AI 都利用遺傳演算法進行編碼。

就個人理解，遺傳演算法是模擬神奇的大自然中生物「優勝劣汰」原則指導下的進化過程，好的基因有更多的機會得到繁衍，這樣一來，隨著繁衍的進行，生物族群會朝著一個趨勢收斂。而生物繁殖過程中的基因雜交和變異會為種群提供更好的基因序列，這樣種群的繁衍趨勢將會是“長江後浪推前浪，一代更比一代強”，而不會是只受限於祖先的最好基因。而程式可以透過模擬這種過程來獲得問題的最優解（但不一定能得到）。要利用這個過程來解決問題，受限需要建構初始的基因組，並為對每個基因進行適應性分數（衡量該基因的好壞程度）初始化，接著從初始的基因組中選出兩個父基因（根據適應性分數，採用*演算法進行選擇）進行繁衍，基於一定的雜交率（父基因進行雜交的機率）和變異率（子基因變異的機率），這兩個父基因會產生兩個子基因，然後將這兩個基因放入族群中，到這裡繁衍一代完成，重複繁衍的過程直到族群收斂或適應性分數達到最大。

接下來我們就來看看用遺傳演算法衝出迷宮的實例。

程式碼如下：

import java.awt.Color;
import java.awt.Graphics;
import java.awt.GridLayout;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
@SuppressWarnings("serial")
public class MazeProblem extends JFrame{
 //当前基因组
 private static List<Gene> geneGroup = new ArrayList<>();
 private static Random random = new Random();
 private static int startX = 2;
 private static int startY = 0;
 private static int endX = 7;
 private static int endY = 14;
 //杂交率
 private static final double CROSSOVER_RATE = 0.7;
 //变异率
 private static final double MUTATION_RATE = 0.0001;
 //基因组初始个数
 private static final int POP_SIZE = 140;
 //基因长度
 private static final int CHROMO_LENGTH = 70;
 //最大适应性分数的基因
 private static Gene maxGene = new Gene(CHROMO_LENGTH);
 //迷宫地图
 private static int[][] map = {{1,1,1,1,1,1,1,1,1,1,1,1,1,1,1},
       {1,0,1,0,0,0,0,0,1,1,1,0,0,0,1},
       {5,0,0,0,0,0,0,0,1,1,1,0,0,0,1},
       {1,0,0,0,1,1,1,0,0,1,0,0,0,0,1},
       {1,0,0,0,1,1,1,0,0,0,0,0,1,0,1},
       {1,1,0,0,1,1,1,0,0,0,0,0,1,0,1},
       {1,0,0,0,0,1,0,0,0,0,1,1,1,0,1},
       {1,0,1,1,0,0,0,1,0,0,0,0,0,0,8},
       {1,0,1,1,0,0,0,1,0,0,0,0,0,0,1},
       {1,1,1,1,1,1,1,1,1,1,1,1,1,1,1}};
 private static int MAP_WIDTH = 15;
 private static int MAP_HEIGHT = 10;
 private List<JLabel> labels = new ArrayList<>();
 public MazeProblem(){
  // 初始化
  setSize(700, 700);
  setDefaultCloseOperation(DISPOSE_ON_CLOSE);
  setResizable(false);
  getContentPane().setLayout(null);
  JPanel panel = new JPanel();
  panel.setLayout(new GridLayout(MAP_HEIGHT,MAP_WIDTH));
  panel.setBounds(10, 10, MAP_WIDTH*40, MAP_HEIGHT*40);
  getContentPane().add(panel);
  for(int i=0;i<MAP_HEIGHT;i++){
   for(int j=0;j<MAP_WIDTH;j++){
    JLabel label = new JLabel();
    Color color = null;
    if(map[i][j] == 1){
     color = Color.black;
    }
    if(map[i][j] == 0){
     color = Color.GRAY;
    }
    if(map[i][j] == 5 || map[i][j] ==8){
     color = Color.red;
    }
    label.setBackground(color);
    label.setOpaque(true);
    panel.add(label);
    labels.add(label);
   }
  }
 }
 @Override
 public void paint(Graphics g) {
  super.paint(g);
  //画出路径
  int[] gene = maxGene.getGene();
  int curX = startX;
  int curY = startY;
  for(int i=0;i<gene.length;i+=2){
   //上
   if(gene[i] == 0 && gene[i+1] == 0){
    if(curX >=1 && map[curX-1][curY] == 0){
     curX --;
    }
   }
   //下
   else if(gene[i] == 0 && gene[i+1] == 1){
    if(curX <=MAP_HEIGHT-1 && map[curX+1][curY] == 0){
     curX ++;
    }
   }
   //左
   else if(gene[i] == 1 && gene[i+1] == 0){
    if(curY >=1 && map[curX][curY-1] == 0){
     curY --;
    }
   } 
   //右
   else{
    if(curY <= MAP_WIDTH-1 && map[curX][curY+1] == 0){
     curY ++;
    }
   }
   labels.get(curX*MAP_WIDTH+curY).setBackground(Color.BLUE);
  }
 }
 public static void main(String[] args) {
  //初始化基因组
  init();
  while(maxGene.getScore() < 1){
   //选择进行交配的两个基因
   int p1 = getParent(geneGroup);
   int p2 = getParent(geneGroup);
   //用*转动法选择两个基因进行交配,杂交和变异
   mate(p1,p2);
  }
  new MazeProblem().setVisible(true);
 }
 /**
  * 根据路径获得适应性分数
  * @param path
  * @return
  */
 private static double getScore(int[] gene){
  double result = 0;
  int curX = startX;
  int curY = startY;
  for(int i=0;i<gene.length;i+=2){
   //上
   if(gene[i] == 0 && gene[i+1] == 0){
    if(curX >=1 && map[curX-1][curY] == 0){
     curX --;
    }
   }
   //下
   else if(gene[i] == 0 && gene[i+1] == 1){
    if(curX <=MAP_HEIGHT-1 && map[curX+1][curY] == 0){
     curX ++;
    }
   }
   //左
   else if(gene[i] == 1 && gene[i+1] == 0){
    if(curY >=1 && map[curX][curY-1] == 0){
     curY --;
    }
   } 
   //右
   else{
    if(curY <= MAP_WIDTH-1 && map[curX][curY+1] == 0){
     curY ++;
    }
   }
  }
  double x = Math.abs(curX - endX);
  double y = Math.abs(curY - endY);
  //如果和终点只有一格距离则返回1
  if((x == 1&& y==0) || (x==0&&y==1)){
   return 1;
  }
  //计算适应性分数
  result = 1/(x+y+1);
  return result;
 }
 /**
  * 基因初始化
  */
 private static void init(){
  for(int i=0;i<POP_SIZE;i++){
   Gene gene = new Gene(CHROMO_LENGTH);
   double score = getScore(gene.getGene());
   if(score > maxGene.getScore()){
    maxGene = gene;
   }
   gene.setScore(score);
   geneGroup.add(gene);
  }
 }
 /**
  * 根据适应性分数随机获得进行交配的父类基因下标
  * @param list
  * @return
  */
 private static int getParent(List<Gene> list){
  int result = 0;
  double r = random.nextDouble();
  double score;
  double sum = 0;
  double totalScores = getTotalScores(geneGroup);
  for(int i=0;i<list.size();i++){
   Gene gene = list.get(i);
   score = gene.getScore();
   sum += score/totalScores;
   if(sum >= r){
    result = i;
    return result;
   }
  }
  return result;
 }
 /**
  * 获得全部基因组的适应性分数总和
  * @param list
  * @return
  */
 private static double getTotalScores(List<Gene> list){
  double result = 0;
  for(int i=0;i<list.size();i++){
   result += list.get(i).getScore();
  }
  return result;
 }
 /**
  * 两个基因进行交配
  * @param p1
  * @param p2
  */
 private static void mate(int n1,int n2){
  Gene p1 = geneGroup.get(n1);
  Gene p2 = geneGroup.get(n2);
  Gene c1 = new Gene(CHROMO_LENGTH);
  Gene c2 = new Gene(CHROMO_LENGTH);
  int[] gene1 = new int[CHROMO_LENGTH];
  int[] gene2 = new int[CHROMO_LENGTH];
  for(int i=0;i<CHROMO_LENGTH;i++){
   gene1[i] = p1.getGene()[i];
   gene2[i] = p2.getGene()[i];
  }
  //先根据杂交率决定是否进行杂交
  double r = random.nextDouble();
  if(r >= CROSSOVER_RATE){
   //决定杂交起点
   int n = random.nextInt(CHROMO_LENGTH);
   for(int i=n;i<CHROMO_LENGTH;i++){
    int tmp = gene1[i];
    gene1[i] = gene2[i];
    gene2[i] = tmp;
   }
  }
  //根据变异率决定是否
  r = random.nextDouble();
  if(r >= MUTATION_RATE){
   //选择变异位置
   int n = random.nextInt(CHROMO_LENGTH);
   if(gene1[n] == 0){
    gene1[n] = 1;
   }
   else{
    gene1[n] = 0;
   }
   if(gene2[n] == 0){
    gene2[n] = 1;
   }
   else{
    gene2[n] = 0;
   }
  }
  c1.setGene(gene1);
  c2.setGene(gene2);
  double score1 = getScore(c1.getGene());
  double score2 = getScore(c2.getGene());
  if(score1 >maxGene.getScore()){
   maxGene = c1;
  }
  if(score2 >maxGene.getScore()){
   maxGene = c2;
  }
  c1.setScore(score1);
  c2.setScore(score2);
  geneGroup.add(c1);
  geneGroup.add(c2);
 }
}
/**
 * 基因
 * @author ZZF
 *
 */
class Gene{
 //染色体长度
 private int len;
 //基因数组
 private int[] gene;
 //适应性分数
 private double score;
 public Gene(int len){
  this.len = len;
  gene = new int[len];
  Random random = new Random();
  //随机生成一个基因序列
  for(int i=0;i<len;i++){
   gene[i] = random.nextInt(2);
  }
  //适应性分数设置为0
  this.score = 0;
 }
 public int getLen() {
  return len;
 }
 public void setLen(int len) {
  this.len = len;
 }
 public int[] getGene() {
  return gene;
 }
 public void setGene(int[] gene) {
  this.gene = gene;
 }
 public double getScore() {
  return score;
 }
 public void setScore(double score) {
  this.score = score;
 }
 public void print(){
  StringBuilder sb = new StringBuilder();
  for(int i=0;i<gene.length;i+=2){
   if(gene[i] == 0 && gene[i+1] == 0){
    sb.append("上");
   }
   //下
   else if(gene[i] == 0 && gene[i+1] == 1){
    sb.append("下");
   }
   //左
   else if(gene[i] == 1 && gene[i+1] == 0){
    sb.append("左");
   } 
   //右
   else{
    sb.append("右");
   }
  }
  System.out.println(sb.toString());
 }
}

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